Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This course introduces participants to the design of end-to-end AI pipelines for omics data, covering data cleaning, integration, feature engineering, predictive modeling, validation, and result interpretation. Across 4 Weeks, you will go deep on data cleaning, feature engineering, and predictive modeling, then consolidate everything in a capstone project. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course introduces participants to the design of end-to-end AI pipelines for omics data, covering data cleaning, integration, feature engineering, predictive modeling, validation, and result interpretation.
📋 Course Objectives
1. Get comfortable working with data cleaning.
2. Build practical fluency in feature engineering.
3. Gain working command of predictive modeling.
4. Apply biotechnology methods to authentic research and industry problems.
5. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
• Data and computational scientists moving into data cleaning
🚀 Key Learning Outcomes
• Confidence to apply data cleaning in real projects.
• Confidence to implement feature engineering in real projects.
• Confidence to reason about predictive modeling in real projects.
• Tangible, reproducible biotechnology work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Omics Data and Its Pathologies
• Transcriptomic, proteomic and metabolomic data shapes and their common formats
• Missing values, detection limits and why deletion biases the result
• Batch effects as the leading cause of irreproducible omics findings
Cleaning and Normalisation
• Normalisation choices — TMM, quantile, VSN — and their differing assumptions
• ComBat and surrogate variable analysis for batch correction, and over-correction risk
• Filtering low-expression features before, not after, statistical testing
Combining Multiple Omics Layers
• Concatenation versus multi-omics factor models such as MOFA
• Sample matching, scale mismatch and the dominance of the largest layer
• Interpreting a latent factor without inventing a biological story for it
Predictive Models on Wide Data
• Regularised models and tree ensembles when p greatly exceeds n
• Nested cross-validation — feature selection inside the fold, never outside
• Multiple testing control with Benjamini-Hochberg and reporting effect sizes
Turning a Signature into an Assay
• Reducing a signature to a panel that a PCR or targeted assay can measure
• Primer design constraints when the analytical target comes from omics data
• Analytical validation: sensitivity, specificity and independent cohort confirmation
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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